Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [60]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [61]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[61]:
<matplotlib.image.AxesImage at 0x20d1b456fd0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [62]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[62]:
<matplotlib.image.AxesImage at 0x20d255d3518>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [63]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.9.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [64]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [78]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    dropout = 0.8
    with tf.variable_scope('discriminator', reuse=reuse):
        # The kernel size is set to 5
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
                              kernel_initializer=tf.contrib.layers.xavier_initializer(),
                              activation=tf.nn.relu)
        x1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(x1 * alpha, x1) # 14x14x64
        x1 = tf.layers.dropout(inputs=relu1, rate=dropout, training=False)
        
        x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same',
                              kernel_initializer=tf.contrib.layers.xavier_initializer(),
                              activation=tf.nn.relu)
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(bn2 * alpha, bn2) # 7x7x128
        x2 = tf.layers.dropout(inputs=relu2, rate=dropout, training=False) 
            
        x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same',
                              kernel_initializer=tf.contrib.layers.xavier_initializer(),
                              activation=tf.nn.relu)
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(bn3 * alpha, bn3) # 4x4x256
        
        #Flatten it
        # flat = tf.reshape(relu3, (-1, 4*4*256))
        flat = tf.contrib.layers.flatten(relu3)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [79]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    dropout = 0.8
    #Reuse the parameters when we are generating the samples but that will not be during the training
    with tf.variable_scope('generator', reuse = not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 3*3*512,
                            kernel_initializer=tf.contrib.layers.xavier_initializer())
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 3, 3, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 3x3x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 512, 4, strides=1, padding='same',
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())  
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        x2 = tf.layers.dropout(inputs=x2, rate=dropout, training=is_train)
        # 3x3x512 now        
                
        x3 = tf.layers.conv2d_transpose(x2, 256, 4, strides=2, padding='same',
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        x2 = tf.layers.dropout(inputs=x2, rate=dropout, training=is_train)
        # 6x6x256 now

        x4 = tf.layers.conv2d_transpose(x3, 128, 4, strides=2, padding='valid',
                                       kernel_initializer=tf.contrib.layers.xavier_initializer())
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        x4 = tf.layers.dropout(inputs=x4, rate=dropout, training=is_train)
        # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=2, padding='same',
                                            kernel_initializer=tf.contrib.layers.xavier_initializer())
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [80]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth=0.1
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # Label Smoothing method only on real data labels
    # d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real)*(1 - smooth))))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [81]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    # Without tf.control_dependencies, TensorFlow’s batch normalization layer will not operate correctly during inference
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [82]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display. 4, 9, 16...other number will be round to squared numbers, I guess.
    :param input_z: Input Z Tensor. It appears here because the inner function "generator" needs it !!!!!
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()
In [98]:
def plot_losses(losses):
    """
    Plot the discriminator and generator losses on the same graph
    :param d_loss: list of loss values for the discriminator
    :param g_loss: list of loss values for the generator
    """
    d_loss = [item[0] for item in losses]
    g_loss = [item[1] for item in losses]
    fig, ax = pyplot.subplots()
    pyplot.plot(d_loss, label='Discriminator', alpha=0.5)
    pyplot.plot(g_loss, label='Generator', alpha=0.5)
    pyplot.title("Training Losses")
    pyplot.legend()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

  • First, load time for recording durations
In [71]:
import time
In [88]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    samples, losses = [], [] # samples is temporarily not used
    steps = 0 ## =batch_no
    print_every=10 ## =stats_every
    show_every=100
    n_images_to_show = 16    
    
    # Build Model, i.e., the tensorflow graph    
    # A graph defines the computation. It doesn’t compute anything, it doesn’t hold any values, it just defines the operations that you specified in your code.
    total_images_count , image_width, image_height, image_channels = data_shape
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels) 
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    total_batches = (total_images_count // batch_size) * epoch_count
    start_time = time.time()
    
    # Run session
    # A session allows to execute graphs or part of graphs. It allocates resources (on one or more machines) for that and holds the actual values of intermediate results and variables
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                batch_images = batch_images * 2 # Because the original Batch_images scale between -0.5 and 0.5, instead of -1 and 1
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers, the generator function will be called twice or thrice since the epoch is fixed to 2 in this project
                # Though this strategy seemed not to improve the image quality generated by the generator....
                # We need to set the input_real placeholder when we run the generator’s optimizer. The generator doesn’t actually use it, but we’d get an error without it because of the tf.control_dependencies block we created in model_opt function.
                _ = sess.run(d_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate}) 
                _ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                # An alternative:
                # _ = sess.run([d_train_opt, g_train_opt, g_train_opt], feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})

                if steps % print_every == 0:

                    current_time = time.time()
                    total_time = current_time - start_time
                    time_per_batch = total_time / steps
                    remaining_time = int(time_per_batch * (total_batches - steps))         
                                        
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z, input_real: batch_images})

                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))           
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "batch {}/{}...".format(steps, total_batches),
                          "time/batch...{:.2f}s".format(time_per_batch),
                          "remaining time...{}s".format(remaining_time),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                if steps % show_every == 0:
                    show_generator_output(sess, n_images_to_show, input_z, image_channels, data_image_mode)

        # show the final output
        show_generator_output(sess, n_images_to_show, input_z, image_channels, data_image_mode)
        return losses, samples                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [100]:
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    losses, _ = train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... batch 10/3750... time/batch...0.53s remaining time...1983s Discriminator Loss: 1.0752... Generator Loss: 4.4392
Epoch 1/2... batch 20/3750... time/batch...0.42s remaining time...1584s Discriminator Loss: 0.5796... Generator Loss: 2.2368
Epoch 1/2... batch 30/3750... time/batch...0.38s remaining time...1411s Discriminator Loss: 1.0352... Generator Loss: 6.0639
Epoch 1/2... batch 40/3750... time/batch...0.36s remaining time...1327s Discriminator Loss: 1.8499... Generator Loss: 0.6951
Epoch 1/2... batch 50/3750... time/batch...0.34s remaining time...1274s Discriminator Loss: 0.4559... Generator Loss: 3.3481
Epoch 1/2... batch 60/3750... time/batch...0.34s remaining time...1241s Discriminator Loss: 0.4478... Generator Loss: 3.0361
Epoch 1/2... batch 70/3750... time/batch...0.33s remaining time...1216s Discriminator Loss: 0.3679... Generator Loss: 5.1964
Epoch 1/2... batch 80/3750... time/batch...0.33s remaining time...1199s Discriminator Loss: 0.4111... Generator Loss: 3.4151
Epoch 1/2... batch 90/3750... time/batch...0.32s remaining time...1183s Discriminator Loss: 0.5017... Generator Loss: 3.3441
Epoch 1/2... batch 100/3750... time/batch...0.32s remaining time...1171s Discriminator Loss: 0.5996... Generator Loss: 2.6625
Epoch 1/2... batch 110/3750... time/batch...0.32s remaining time...1171s Discriminator Loss: 1.6407... Generator Loss: 0.5142
Epoch 1/2... batch 120/3750... time/batch...0.32s remaining time...1156s Discriminator Loss: 0.4540... Generator Loss: 3.8213
Epoch 1/2... batch 130/3750... time/batch...0.32s remaining time...1144s Discriminator Loss: 0.4634... Generator Loss: 3.1284
Epoch 1/2... batch 140/3750... time/batch...0.31s remaining time...1133s Discriminator Loss: 0.5819... Generator Loss: 2.8359
Epoch 1/2... batch 150/3750... time/batch...0.31s remaining time...1124s Discriminator Loss: 0.7079... Generator Loss: 9.3455
Epoch 1/2... batch 160/3750... time/batch...0.31s remaining time...1114s Discriminator Loss: 0.5193... Generator Loss: 2.5955
Epoch 1/2... batch 170/3750... time/batch...0.31s remaining time...1107s Discriminator Loss: 0.5027... Generator Loss: 2.6596
Epoch 1/2... batch 180/3750... time/batch...0.31s remaining time...1100s Discriminator Loss: 0.4011... Generator Loss: 4.4067
Epoch 1/2... batch 190/3750... time/batch...0.31s remaining time...1093s Discriminator Loss: 0.6032... Generator Loss: 1.8928
Epoch 1/2... batch 200/3750... time/batch...0.31s remaining time...1087s Discriminator Loss: 0.4009... Generator Loss: 3.8817
Epoch 1/2... batch 210/3750... time/batch...0.31s remaining time...1090s Discriminator Loss: 0.4891... Generator Loss: 2.6749
Epoch 1/2... batch 220/3750... time/batch...0.31s remaining time...1084s Discriminator Loss: 0.4251... Generator Loss: 7.2163
Epoch 1/2... batch 230/3750... time/batch...0.31s remaining time...1079s Discriminator Loss: 0.3783... Generator Loss: 3.9342
Epoch 1/2... batch 240/3750... time/batch...0.31s remaining time...1074s Discriminator Loss: 0.4434... Generator Loss: 2.8741
Epoch 1/2... batch 250/3750... time/batch...0.31s remaining time...1069s Discriminator Loss: 0.6423... Generator Loss: 1.9908
Epoch 1/2... batch 260/3750... time/batch...0.31s remaining time...1065s Discriminator Loss: 0.4478... Generator Loss: 3.7369
Epoch 1/2... batch 270/3750... time/batch...0.30s remaining time...1060s Discriminator Loss: 0.6031... Generator Loss: 2.6018
Epoch 1/2... batch 280/3750... time/batch...0.30s remaining time...1056s Discriminator Loss: 0.3882... Generator Loss: 4.3794
Epoch 1/2... batch 290/3750... time/batch...0.30s remaining time...1052s Discriminator Loss: 0.5095... Generator Loss: 3.8878
Epoch 1/2... batch 300/3750... time/batch...0.30s remaining time...1047s Discriminator Loss: 1.5443... Generator Loss: 0.6464
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In [101]:
plot_losses(losses)

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [102]:
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    losses, _ = train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... batch 10/6331... time/batch...0.56s remaining time...3516s Discriminator Loss: 0.4450... Generator Loss: 2.6662
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In [103]:
plot_losses(losses)

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.